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Learning can begin with labelled examples
Supervised learning learns relationships between inputs and known target labels.
The idea
Training examples guide a model toward predictions for new inputs. The usefulness of those predictions depends on data and evaluation.
An example
A spam classifier learns from emails labelled spam or not spam.
Put it into practice
Ask how the labels were created and whether new cases resemble the training data.
Further reading: Google · Machine Learning ↗
Test yourself: Which dataset supports supervised learning?
Emails paired with spam or not-spam labels
Known target labels provide the learning signal.
Explanation and illustrative examples by One Card Wiser. Source linked for further reading.
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